1. Field of the Invention
Methods and apparatuses consistent with the present invention relate to processing an image by using a bit-plane, and more particularly, to converting a lower bit-plane image, and to inverse-converting the lower bit-plane image, which can increase correlation of the lower bit-plane image.
2. Description of the Related Art
Processing an image by using a bit-plane image is performed by dividing a multi-level image into a group of several binary images, and then processing each divided binary image. A pixel value p of a pixel in m bits may be expressed according to an equation, p=am-1*2m-1+am-2*2m-2+ . . . +a1*21+a0*20, which is in a form of a polynomial having a base of 2. When each pixel value p is expressed in a bit string including m bits as in the above equation, a method of dividing an input image into bit-plane images can be performed by dividing an input image into bit-plane images, which are planes formed of the n-th bit with regards to a bit string of a pixel value of each pixel of the input image, wherein 1≦n≦m and n is an integer. When a pixel value is expressed in a bit string having m bits, the first bit of the bit string is a most significant bit (MSB), and the last m-th bit of the bit string is a least significant bit (LSB). Accordingly, when an image is divided into bit-plane images, m bit-plane images each including a single bit from MSB to the LSB of the bit string of each pixel in the image are formed.
FIG. 1 is a diagram illustrating an input image 10 divided into bit-plane images according to a related technology. Here, reference numerals 11 through 18 denote a zeroth bit-plane image, a first bit-plane image, a second bit-plane image, a third bit-plane image, a fourth bit-plane image, a fifth bit-plane image, a sixth bit-plane image, and a seventh bit-plane image, respectively.
Referring to FIG. 1, when each pixel value of the input image 10 is expressed by using eight bits, the zeroth bit-plane image 11 includes an a0 bit, which is the LSB of each pixel of the input image 10, and a (m−1)-th bit-plane image includes an am-1 bit, which is the m-th bit of each pixel of the input image 10. Referring to lower bit-plane images, i.e., the zeroth through third bit-plane images 11 through 14, from among the bit-plane images illustrated in FIG. 1, it can be seen that a correlation or similarity between the zeroth through third bit-plane images 11 through 14 is remarkably low. This is because even when adjacent pixels have a correlation or similarity in the input image 10, pixel values change when only low bits are separated from the original pixel values, thereby decreasing the correlation. For example, if it is assumed that pixel values of adjacent pixels in the input image 10 are 127, 127, 128, 128, 128, 128, and 128, then it is clear that they are very similar. Here, a pixel value 127 expressed in a binary is 01111111, and a pixel value 128 expressed in a binary is 10000000. When the pixel value is 127 in the input image 10, a pixel value of a pixel corresponding to the third bit-plane image 14 has a value of 15 corresponding 1111 that is the four low bit strings, and when the pixel value is 128 in the input image 10, a pixel value of a pixel corresponding to the third bit-plane image 14 has a value of 0 corresponding to 0000 that is the four low bit strings. Accordingly, the correlation of the pixels in the input image 10 may be poor in a lower bit-plane image when bit-plane dividing is performed. As illustrated in FIG. 1, it is generally difficult to find any regularity in the lower bit-plane images, and the correlation between pixels in each lower bit-plane image is low. Consequently, the lower bit-plane images having low correlation are not suitable to be processed, for example, not suitable for image compression, because the lower bit-plane images lack regularity.